Abstract:Epithelial ovarian cancer (EOC) is one of the most lethal gynecological cancers worldwide. EOC cells educate tumor-associated macrophages (TAM) through CD44-mediated cholesterol depletion to generate an immunosuppressive tumor microenvironment (TME). In addition, tumor cells frequently activate Notch1 receptors on endothelial cells (EC) to facilitate metastasis. However, further work is required to establish whether the endothelium also influences the education of recruited monocytes. Here, we report that cano… Show more
“…There is literature indicating that in a mouse model with subcutaneous injection of human ovarian cancer cells, tumor volume significantly decreased in animals injected with interferon and monocytes at the early stage of tumor formation, and in some animals, tumors completely disappeared [ 50 ]. Stimulating the expression of CD44 in monocytes promotes apoptosis of ovarian cancer cells, favoring immune suppression [ 51 ]. Monocytes in the blood, besides their immune functions, also belong to a complex tissue control system (TCS).…”
Background
There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.
Methods
Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.
Results
The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.
Conclusion
This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.
“…There is literature indicating that in a mouse model with subcutaneous injection of human ovarian cancer cells, tumor volume significantly decreased in animals injected with interferon and monocytes at the early stage of tumor formation, and in some animals, tumors completely disappeared [ 50 ]. Stimulating the expression of CD44 in monocytes promotes apoptosis of ovarian cancer cells, favoring immune suppression [ 51 ]. Monocytes in the blood, besides their immune functions, also belong to a complex tissue control system (TCS).…”
Background
There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.
Methods
Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.
Results
The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.
Conclusion
This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.
“…And RASSF1 is one of the most frequently reported methylationinactivated tumor suppressor genes in primary pancreatic ductal adenocarcinomas (PDAC) 45 . "RBPJ"-It was reported that RBPJ-dependent tumor-associated macrophages (TAMs) in promoting tumor immune tolerance in part by modulating the CD8 + T cell response 46,47 . "SLC35F2"-It might be highly expressed in aggressive and invasive tumor cells 48 and was revealed an essential role for the expression of SLC35F2, a solute carrier protein for YM155 uptake, which leads to DNA damage 49 .…”
Purpose
Pancreatic adenocarcinoma (PAAD) is a deadly disease, particularly for those with diabetes mellitus (DM). While there have been various studies on prognostic factors in pancreatic cancer, few have specifically focused on PAAD patients with DM. This study aimed to identify differentially expressed genes (DEGs) between DM and non-DM individuals and develop a predictive model.
Materials and Methods
PAAD patients with DM were divided into training (70%) and test (30%) groups, and OS-associated genes were identified using univariate COX analysis. A 10-gene risk model was constructed using LASSO-penalized COX regression with ten-fold cross-validation.
Results
The model showed a C-index of 0.83 in the training group and 0.76 in the test group. High risk group represented a tumor-growth and angiogenic phenotype and the low group represented an immune-active phenotype.
Conclusion
This prognostic model holds promise for predicting overall survival in PAAD patients with DM, indicating potential benefits from immunotherapy for those with low-risk scores.
“…Recently, a single-cell study reported that foetal-associated PLVAP + endothelial cells and foetal-like FOLR2 + macrophages colocalized in the liver tumour ecosystem to form an immunosuppressive niche, in which PLVAP + ECs activate Notch signalling in macrophages via specific ligand‒receptor interactions (DLL4:NOTCH2) to promote TAM reprogramming [ 48 ]. In addition, the EC-derived cytokine CXCL2 could drive macrophage recruitment and induce an immunosuppressive phenotype to suppress T-cell cytotoxicity [ 136 ]. Fig.…”
Macrophages and tumour stroma cells account for the main cellular components in the tumour microenvironment (TME). Current advancements in single-cell analysis have revolutionized our understanding of macrophage diversity and macrophage–stroma interactions. Accordingly, this review describes new insight into tumour-associated macrophage (TAM) heterogeneity in terms of tumour type, phenotype, metabolism, and spatial distribution and presents the association between these factors and TAM functional states. Meanwhile, we focus on the immunomodulatory feature of TAMs and highlight the tumour-promoting effect of macrophage–tumour stroma interactions in the immunosuppressive TME. Finally, we summarize recent studies investigating macrophage-targeted therapy and discuss their therapeutic potential in improving immunotherapy by alleviating immunosuppression.
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